The digital representation of a visual image by digitally encoding each picture element making up the image generally requires a great quantity of image data. To process and store such image data requires digital processors with large storage capacities, which tends to increase the cost of such processors. Rapid transmission of such quantities of digital image data requires high-capacity data-transmission channels, which generally tend to be expensive. Transmission of such image data on lower-capacity data-transmission channels can be impractically slow.
A number of schemes have been devised for "compressing" digital data which represents a digitized visual image by encoding the data in such a way that the original image--or a visually-acceptable facsimile of the image--can be reconstructed from the encoded data even though the quantity of the image data is reduced relative to a direct digital representation of each picture element of the image. Conventional compression schemes for continuous-tone image data which do not entail a loss of image quality typically achieve data-compression ratios of three to one or less. Frequently, the data-compression ratios are less than two to one.
In many applications image data from a sequence of related images must be processed. The images in such a sequence are often highly correlated. For example, in processing typical multi-band radar images, hundreds of different images of the same scene are recorded, each corresponding to a different spectral band. Sequences of images representing motion--such as the images forming the frames of digital television--also tend to be highly correlated.
As disclosed in the book Computer Image Processing and Recognition by Ernest L. Hall (Academic Press, 1979), pages 342 through 367, predictive and interpolative coding techniques have been proposed for transmitting digital television images. As explained on pages 345 and 346, in a general predictive coding system, an equation of prediction is assumed between a present picture value and n-1 past picture values. Minimizing a mean squared error between the actual picture values and the predicted values gives an optimum prediction method. One form of predictive image coding system includes an encoder which computes an error between a current value and a predicted value. The error is quantized and transmitted through a communication channel to a decoder system. The decoder system forms a received signal by adding the quantized error to the computed predicted value. In one predictive coding technique for digital television described on pages 355 and 356 of the Hall book, each picture element in a digital television frame is predicted using a set of previously scanned elements. Specifically, the differential pulse code modulation ("DPCM") predictor f(x,y,t) for the picture element f(x,y,t) at location x,y at time t is written: EQU f(x,y,t)=.alpha.f(x-1,y,t)+.beta.f(x,y-1,t)+.gamma.f(x,y,t-1).
The prediction parameters .alpha., .beta., .gamma. are selected to minimize a mean squared prediction error. According to the Hall book, by expressing the mean squared error in terms of correlation values and differentiating with respect to the prediction parameters, a set of algebraic equations is given from which the desired prediction parameters can be found.
Although the predictive and interpolative coding disclosed in the book by Hall cited above provides a limited increase in efficiency in coding digital television images, a need exists for greater efficiency in processing sequences of images--particularly sequences of highly correlated images such as the images corresponding to the bands of multi-band ground survey radar.